Why does the capital market first replicate future trends before completing pricing?

Source: insights4vc; Compiled by: Shaw, Jinse Finance

The pricing trajectory of the capital market for the future is not always smooth and linear. At major technological and financial turning points, the market often begins pricing with preliminary estimates. Investors will select targets with good liquidity, tradability, and that meet institutional investment standards, betting on generally favored future trends, but at this time, they cannot rely on traditional fundamentals to complete value verification. Before the complete economic logic of the industry is clear, the premium generated by such targets is a mimicry premium.

Market judgment deviations usually do not stem from the vagueness of future trends. Railways, electrification, fiber optic networks, the Internet, programmable settlement, and artificial intelligence—these development directions have all been realized in reality.

A more common mistake lies in misjudging the market's entry point: capital rushes into the wrong targets too early, bets on non-core segments of the industry chain, or the target's debt structure cannot withstand the impact of price revaluation. Even if the industry's vision is realized as expected, early popular alternative targets may not be able to retain their value dividends. This difference in perception will become increasingly critical by mid-2026. Discussions in the field of artificial intelligence are no longer limited to whether market demand truly exists. Nvidia's Q1 FY2027 financial report, capital expenditures of large technology companies, semiconductor supply gaps, and rising data center energy consumption all confirm that the industry is in a genuine upward cycle of installations. Nvidia's revenue reached $81.6 billion in that quarter, with data center revenue at $75.2 billion, a non-GAAP gross margin of 75%, and a Q2 revenue forecast of $91 billion. Google, Meta, Microsoft, and Amazon have also invested heavily, far exceeding the scale of conventional software business expansion, essentially focusing on industrial-grade infrastructure construction. Therefore, the core controversy is no longer the industry value of artificial intelligence, but rather which layers of the artificial intelligence industry chain can maintain a long-term profit advantage after the premium of leading benchmark assets gradually returns to normal. The same issue exists in the cryptocurrency field. As of May 2026, stablecoins have long since deviated from their initial positioning as settlement tools in the crypto market. Its market size is estimated at $315 billion to $320 billion, the vast majority denominated in US dollars, with market share highly concentrated in two major cryptocurrencies: USDT and USDC. As of March 31, 2026, USDT disclosed approximately $183 billion in token-related liabilities, including $141 billion in direct and indirect exposure to short-term US Treasury bonds. Circle data shows that as of May 18, 2026, the circulating supply of USDC was $76.8 billion, with the vast majority of reserve assets held in the Circle Reserve Fund, managed by BlackRock and custodied by Bank of New York Mellon. Stablecoins are no longer merely alternatives to crypto assets; these private liability vehicles, reserve asset structures, and circulation channels are vying for practical value in payment settlement, collateral, fund management, and cross-border clearing. This article does not deny the true value of artificial intelligence, stablecoins, and institutional-grade crypto assets. Its core discussion revolves around which aspects can retain long-term profit value after market valuation restructuring. Superficial imitation only chases future outward appearances: trading codes, category labels, asset packaging, token forms, AI concept packaging, institutional endorsement influence, and convenient financing structures. Structural imitation focuses on the underlying core elements supporting industry implementation: computing power density, advanced packaging technology, network communication, energy supply, reserve systems, redemption credibility, compliance systems, circulation channels, settlement standards, conversion costs, and physical infrastructure. Industry development trends are often realistically predictable, but most early followers in the market struggle to maintain a long-term foothold. The value difference between the two is the imitation premium. From a practical perspective, the imitation premium refers to the additional benefits an asset, company, token, industry, or asset encapsulation vehicle receives when it becomes a representative target for investors to position themselves in future trends, and its true value cannot yet be determined based on traditional fundamentals. These benefits include valuation premiums, liquidity, market attention, ease of financing, and institutional recognition. It is not only reflected in price differences but can also manifest as higher trading valuation multiples, lower financing costs, faster private fundraising, enhanced secondary market liquidity, inclusion in indexes, institutional endorsement, benchmark-driven effects, increased regulatory tolerance, and the sense of investment security brought by mature asset forms. The imitation premium arises when the market no longer prices solely based on expected cash flows but instead favors targets with potential future prospects. This mechanism can explain why investors frequently misjudge groundbreaking industry trends. Bubbles are often attributed to irrational speculation in hindsight, but a more accurate interpretation is that the market is forming a consensus around an unknown future by using imperfect alternatives. Shiller's narrative economics explains how communicative narratives influence market perception and behavior; Banerjee, Bicandani, and others' research demonstrates that herd mentality and information cascading effects can overwhelm individual private judgments; Solnet reveals that positive feedback can generate super-exponential price fluctuations; Minsky's theory can distinguish between asset demand and liability vulnerability; Perez points out that speculative capital will flood in early stages of industrial development; David, Yovanovitch, and Rousseau point out that the capacity gains of general-purpose technologies often have a long-term lag. In summary, bubbles are not simply valuation errors, but rather the product of consensus coordination at the socio-financial level. Because the future direction anchored by this consensus is still unclear, market trends are prone to excessive deviation. For institutional investors, the imitation premium rarely concentrates solely on the ultimate winner. While some popular benchmark stocks do indeed possess core structural barriers to entry in their industries, more often than not, these stocks are either upstream in these barriers, downstream in the cash flow realization process, or adjacent to key regulatory control points. This also confirms historical patterns: the future vision predicted by the market often deviates from the actual economic benefits realized. After the collapse of railway stocks, the railway industry still survived; after early market enthusiasm far exceeded investment returns for a long time, electrification truly revolutionized production efficiency; telecommunications companies faced financial crises, but the over-deployed fiber optic networks preserved long-term usable transmission capacity; and now, with massive investment in the artificial intelligence industry, even if the valuation bubbles of some individual stocks burst, valuable infrastructure will still remain. The clearest distinction at the structural level lies in the difference between superficial imitation and structural imitation. Superficial imitation merely replicates the outward appearance of the future, applying market-recognized concepts, forms, asset encapsulation models, and token forms that represent industry change. Structural imitation, on the other hand, involves investing capital, building and reconstructing the underlying economic logic, and truly anchoring the fundamental elements that possess core value after the industry's implementation. From a philosophical perspective, the former can be called Platonic imitation. However, for investment decision-making teams, structural imitation is a more practical term. This concept allows analysis to focus on industry barriers, debt structures, and market patterns, rather than being confined to abstract theories. This difference between surface and deep layers is evident in many fields, both now and in the past. Bitcoin created a monetary asset and settlement foundation with scarcity and censorship resistance; subsequent token issuances merely copied the token form but lacked monetary credibility, sufficient liquidity, and long-term practical value. While decentralized finance (Decentralized Finance) has flaws in governance mechanisms and risk management, its essence is to genuinely reconstruct financial functions such as trading, collateralized lending, and market making; whereas initial coin offerings (ICOs) are mostly just packaging immature products as financing tools. In the US stock market's AI sector, Nvidia is both a benchmark AI stock favored by investors and genuinely possesses core technological barriers in areas such as accelerated computing power, software integration, and even network communications; many companies that merely label themselves with AI in their business outlooks have not truly controlled the core links of the industry chain. In the stablecoin sector, reserve size, redemption credit, asset custody, compliance systems, and circulation channels are far more crucial than simply the form of crypto dollars. During the special purpose acquisition company (SPO) boom, these listing vehicles allowed venture capital narratives to enter the public market, but they could not guarantee that the company would possess core industry barriers. Historical Case Review: The railway speculation boom is a typical early example of betting on the future of a real industry based on misvalued assets. By 1845, the British railway industry had reached a considerable economic scale, with revenue accounting for over 1% of GDP. Market estimates predicted that this figure would exceed 10% after expansion. According to O'Dleitzko's calculations, railway investment reached 7.3% of GDP in 1847, while the railway stock index fell from 167.9 points in July 1845 to 60.5 points in October 1849. O'Dleitzko's core conclusion was not that the railway industry was a false boom, but rather that by mid-1846, sufficient information already demonstrated that overall market expectations of returns were out of touch with reality, while the innovation in transportation itself possessed real value. The market accurately predicted the development direction of faster transportation and expanded commercial reach, but repeatedly confused industry trends with the profitability of individual lines, the strength of project initiators, and passenger flow forecasts. What ultimately survived was the railway network, while the valuations of many investments relying on it collapsed. The history of electrification illustrates that delayed realization of industrial benefits does not equate to the absence of technological innovation. David's comparison of computers and generators is highly relevant. Around 1900, electrical equipment was ubiquitous, yet it failed to translate into economic data. The outdated factory systems built on steam power and drive shafts needed restructuring to unleash the full potential of electricity. David points out that the delayed capacity release stemmed from the large stock of aging production equipment and was also constrained by the overall need for restructuring production models. Jovanovich and Rousseau add that the electrification wave, similar to the subsequent information technology revolution, spawned a large number of new entrants, leading to asset reallocation and a shift in market leadership. During the electrification phase, the market capitalization of new entrants far exceeded their capital investment, and new technologies allowed emerging companies to seize market share, diminishing the advantages of established companies. Historical patterns are clear: technological breakthroughs are real, but commercial returns come later than expected, and the implementation models differ significantly from initial market assumptions. The telecommunications fiber optic industry cycle in the late 1990s occurred between the excessive railway construction and the wave of modern digital infrastructure development. Research by the OECD and the Federal Reserve Bank of Richmond shows that relaxed industry regulations, technological innovation in optical communications, and the promising prospects of mobile communications and the internet drove large-scale borrowing, equity issuance, and infrastructure investment. In 2000, telecommunications infrastructure investment in OECD countries approached $230 billion, accounting for 4% of total corporate fixed asset investment; subsequently, industry investment contracted sharply. During the same period, industry traffic forecasts were generally overly optimistic. Odletzko argued that based on the claim that internet traffic doubles every 90 to 100 days, the user base in 2000 would have been severely out of sync with reality, with a reasonable growth rate of approximately doubling annually. However, this does not mean that the related infrastructure was worthless. Industry history records that the breakthroughs in fiber optic communication and network technology over the past decade laid a solid foundation for the development of the global internet, even during periods of industry downturn when companies generally suffered losses. This is a typical characteristic of imitation premiums: capital pays for the premium of expected network growth, objectively building infrastructure with long-term social and commercial value. The internet bubble is often simply interpreted as a speculative farce fueled by unprofitable websites, but in reality, there are clear differences in underlying mechanisms. The internet is a genuine, universal, and fundamental platform, but having an internet-related stock is not the same as having a sustainable internet profit model. Research by Jovanovich and Rousseau on the information technology era shows that during periods of technological innovation, industry entry barriers decrease, asset turnover accelerates, and market leaders change frequently. Related research further clarifies the core issue: the market often follows and imitates the superficial characteristics of the internet, ignoring the underlying logic that creates a company's core competitiveness. The real dividing line lies not in whether a company belongs to the internet industry, but in whether it can control the advantages of increasing channel scale, cost barriers, network ecosystems, or data architectures where more users equal higher value. The tulip mania analogy is inaccurate; most internet industry visions have materialized as expected. The problem lies in the market's flawed judgment regarding the distribution of industry dividends. The wave of mergers and acquisitions in the 1960s was not a technology bubble, but rather a speculative frenzy fueled by corporate management models and stock market valuation systems. Schriver and Vischny proposed the stock market-driven M&A theory, during which overvalued companies typically acquired relatively undervalued companies using stock as consideration. Hubbard and Palia further analyzed that at that time, external capital markets were still imperfect, corporate information disclosure capabilities were weak, and internal corporate fund allocation systems had real value. The market's pursuit of diversified group structures and M&A financing models essentially stemmed from the belief that these models could alleviate financing difficulties and information asymmetry. The subsequent bursting of the bubble proved that relying solely on business diversification and brand management strength could not consistently generate excess returns. The Japanese asset bubble of the late 1980s fostered an alternative, imitative premium centered on asset permanence, national strength, and collateral value. Research by the Bank for International Settlements indicates that aggressive financial operations, prolonged monetary easing, relaxed industry regulations, and a lack of risk management, coupled with fiscal and tax policies that fueled land price increases and market optimism, collectively triggered the crisis. The market generally believed that Japan had entered a new stage of economic development, but its highly dependent financial structure, heavily reliant on bank credit and real estate as collateral, ultimately amplified the impact of the collapse. This speculation was not about following new technologies, but rather about pursuing the asset's value preservation and perpetual worth. Land assets, bank collateral, and assets in prime urban locations became symbolic targets for the market's bets on Japan's long-term economic prosperity. While the market sought asset stability, it was actually embracing a highly leveraged chain of mortgage debt. The US housing and structured credit crisis followed a similar pattern. Levitin and Wacker argue that the housing bubble stemmed from an oversupply of mortgages, leading to distorted pricing of a large number of mortgage assets. Private mortgages supported the expansion of securitization, exacerbating information asymmetry and leading to increasingly lax mortgage approval standards. Gordon and Metrick's research found that risk spread through the securitized banking system, with repurchase financing and mortgage discount rates corresponding to deposit business and reserve requirements in the traditional banking system. The market's current focus is on stable returns; high-rated collateralized assets, tiered structured products, and compliant institutional packaging are all seen as safe havens. However, the underlying structure is actually a fragile debt chain. The seemingly stable asset value relies entirely on market confidence in the collateral, which itself is complex and has weak risk resistance. Ultimately, the aura of stability surrounding privately packaged assets disappeared, and some securitization infrastructure survived under stringent regulation. The surge in Special Purpose Acquisition (SPAC) companies from 2020 to 2021 satisfied the market's demand for high-return venture capital investments through simplified listing platforms. Related research indicates that this type of listing structure presents significant agency contradictions: the initiator obtains equity returns at extremely low cost, shareholders can redeem their shares at the offering price, and even if the acquired company performs poorly, the initiator can still profit. Mechanisms such as performance compensation, equity concessions, equity dilution, and share redemption create complex interest flows among the initiator, the acquired target, private equity investors, and ordinary shareholders. SPACs have become a popular vehicle for narratives in emerging sectors, particularly in cutting-edge fields such as electric vehicles, aerospace, and autonomous driving, providing a shortcut to listing for high-risk venture capital projects. However, in essence, the listing shell is not a core industry barrier, but merely a financing model. When the market's pursuit of conceptual value far exceeds the business fit, this form of financing generates a premium. Meme stocks have compressed their speculative cycle to just a few weeks. A report from the U.S. Securities and Exchange Commission (SEC) on the market performance at the beginning of 2021 shows that GameStop's stock price and trading volume fluctuated significantly, short positions remained high, and discussions on social media and mainstream media surged. Online discussions incorporated fundamental analysis, expectations for the company's transformation, and even instances of coordinated short squeezes. The report also clarified the driving logic behind the market: short covering only provided a temporary boost to prices, accounting for a limited proportion of the overall buying volume, and there was no evidence that a gamma-ray short squeeze was the core trigger. The stock price's sustained rise over several weeks was mainly driven by market optimism. The meme stock phenomenon confirms that public opinion, group perception, short positions, and trading mechanisms can make a company a social media hotspot and generate a valuation premium, while the company's fundamental business operations have not fundamentally changed. Looking at various cases, one pattern is obvious: bubbles and frenzy often arise from real development trends, not from unfounded fantasies. Railways, electrification, fiber optic networks, and the internet all possess significant practical value. Asset securitization, digital payments, and some blockchain-based monetary innovations, though operating on different tracks, are essentially the same. Therefore, the focus of industry discussions should no longer be on the authenticity of industry visions, but rather on a more discerning question: after the conceptual premium fades, which link in the industrial chain can firmly control the actual economic benefits? The AI ​​Industry's Implementation Stage: Currently, the assessment of the AI ​​industry needs to be more objective and cautious than the mainstream views of 2023-2024. The core issue in 2026 is no longer whether there is abstract market demand. Nvidia's latest quarterly financial report, the large-scale capital expenditures of tech giants, and the implementation of AI in enterprise business scenarios all make the argument that "AI is purely a bubble" untenable. The real key issue is whether the market is conflating the actual scale of industry implementation with the reasonable valuation of individual stocks. The probability of the AI ​​industry experiencing sham development has decreased, but the risk of mismatched valuations for individual stocks remains high. Nvidia is a typical example of a stock that combines the attributes of a concept stock with core competitive advantages. In the first quarter of fiscal year 2027, Nvidia's revenue reached a record high of $81.6 billion, with data center revenue reaching $75.2 billion; GAAP and non-GAAP gross margins were 74.9% and 75%, respectively. Even excluding revenue from computing power services in the Chinese market, the company's second-quarter revenue guidance is still as high as $91 billion. Breaking down the business segments, computing power revenue was $60.4 billion, and networking revenue was $14.8 billion. Management defined this round of industry cycle as the large-scale construction of AI factories, pointing out that intelligent agent AI has already created real commercial value. The company also launched the Vera Rubin platform and released Power 1.0 software, adapted for Blackwell graphics cards and used for generative and intelligent agent inference tasks. The company also added an $80 billion stock buyback program and increased its quarterly dividend 25 times to $0.25 per share. The data clearly demonstrates that Nvidia is not simply riding the hype wave, but rather positioned in the rapidly expanding infrastructure sector, holding an irreplaceable core competitive advantage. However, this financial report also confirms the practical significance of the imitation premium theory. Nvidia is the most liquid AI-related stock, attracting funds not only from fundamental investors but also from index allocation needs, institutional hedging, and speculative capital. Reuters points out that as industry competition extends from model training to inference applications, the impact of leading technology companies developing their own chips and the pressure on inference business profitability are gradually emerging. This has not weakened Nvidia's core industry position; it simply illustrates that the same company can simultaneously possess the dual attributes of an industry barrier controller and a speculative premium target. Even with increased stock price volatility and valuation corrections, the industrial value at the infrastructure level remains solid; the two are not contradictory. The semiconductor and related infrastructure industries exhibit a similar trend. TSMC projects its AI-related revenue will maintain a CAGR of around 45% over the next five years; after doubling its co-packaging capacity in 2025, it plans to double that capacity again in 2026. ASML's net orders reached €3.9 billion in Q1 2026, with long-term market demand still driven by the AI ​​industry. Broadcom's AI-related revenue exceeded $4.4 billion in Q1, a significant year-on-year increase of 77%, with both its self-developed accelerator chips and network equipment businesses contributing to the growth. AMD's data center business revenue reached $5.8 billion, a year-on-year increase of 57%, with inference applications and intelligent agent technology being the core growth drivers. Micron achieved a record high quarterly revenue, with its high-bandwidth memory products boosted by demand from AI servers; its 2026 capacity is fully booked, and its 2027 capacity is also largely booked. Cisco's data center switch orders increased by over 40% year-on-year, and it previously disclosed that its AI infrastructure orders from leading technology companies reached $2.1 billion. Applied Materials remains optimistic about the investment boom in wafer foundry and memory chips driven by artificial intelligence and high-bandwidth memory. The entire industry chain shows a unified trend: industry value is no longer concentrated solely in popular areas like graphics card chips, but is gradually extending to packaging, storage, networking, optical components, and production equipment. This downward penetration of industry value from the surface of popular sectors is precisely the trend predicted by the imitation premium theory. In the early stages of the market's pursuit of emerging industries, funds tend to flock to the most recognizable themes; as the industry matures, the focus of profit will gradually shift to the less-discussed but irreplaceable underlying segments. The artificial intelligence industry is energy-intensive, has limited packaging technology, and is highly dependent on network and storage devices, exhibiting strong physical hardware attributes. The International Energy Agency's benchmark forecast shows that global data center electricity consumption will approach 945 terawatt-hours by 2030, with the United States accounting for nearly half of the increase. In some parts of the United States, grid connection queues can take 3 to 7 years, while data center construction takes only 18 to 24 months, resulting in severe shortages of grid maintenance personnel, transformer equipment, and supporting infrastructure. These development constraints do not diminish the demand for artificial intelligence; rather, they enhance the strategic value of those who control basic infrastructure resources. The investment strategies of tech giants also corroborate this trend. Google's capital expenditure in Q1 2026 was $35.7 billion, with the vast majority invested in AI technology infrastructure, including 60% in servers and 40% in data centers and network facilities; full-year capital expenditure is expected to remain between $175 billion and $185 billion. Meta's Q1 capital expenditure was $19.84 billion, with its full-year forecast revised upward to $125 billion to $145 billion, primarily driven by hardware price increases and data center expansion. Microsoft's capital expenditures in the second quarter of fiscal year 2026 reached $37.5 billion, with two-thirds allocated to short-term depreciation assets such as graphics cards and processors. Last year, in the third quarter, it also stated that only half of its investments in cloud computing and artificial intelligence were in long-term assets. This type of financial disclosure allows industry profit return analysis to move beyond simply focusing on expenditure growth and instead concentrate on asset lifespan, depreciation rates, financing lease models, asset obsolescence risks, and equipment utilization rates. While the industry's implementation phase is real, it often struggles to generate stable returns in the early stages. Amazon did not disclose detailed full-year capital expenditures, but its cloud service revenue in the first quarter increased by 28% year-on-year, reaching $37.6 billion. The company's 2025 shareholder letter mentioned that in the first quarter of 2026, annualized revenue from its cloud service and artificial intelligence business exceeded $15 billion and continued to maintain rapid growth. Cloud computing channels are one of the few sectors that do not rely on chips but possess extremely strong bargaining power. The industry landscape is no longer dominated by a single company; the importance of self-developed chips and business diversification continues to rise. Take Anthropic as an example: its models utilize hardware platforms from Amazon, Google, and NVIDIA. While deepening its cooperation with Amazon and securing 50 gigawatts of computing power, it also expands its computing power cooperation with Google and Broadcom, deploying next-generation training chips on a large scale. Top model companies are no longer tied to a single supplier, but are beginning to flexibly choose and optimize their hardware systems. NVIDIA represents the changes in the upstream hardware landscape, while Anthropic reflects the progress of downstream commercialization. In February 2026, Anthropic completed a $30 billion Series G funding round, valuing the company at $380 billion post-investment; its Code Intelligence application's annualized revenue exceeded $2.5 billion. As of April, the company's overall annualized revenue climbed to $30 billion, a significant increase from $9 billion at the end of 2025; the number of enterprise customers with annual payments exceeding one million US dollars exceeded 1,000. The company has also established a global partnership with KPMG, covering 276,000 employees; collaborated with PwC to promote intelligent code tools, serving tens of thousands of professionals; and launched an intelligent plugin for financial business compatible with Microsoft Office suite, empowering work scenarios such as programming, auditing, transaction research, document drafting, and credit review. The relevant data is disclosed by the company itself and has not yet been audited, but it is sufficient to prove that existing computing resources have effectively met the company's real business needs. This is also a reasonable assessment logic for intelligent office scenarios in the financial sector. The industry value lies not in creating AI companies that are simply geared towards banks, but in integrating technology into high-value workflows such as code development, due diligence, compliance review, credit underwriting, month-end settlement, solution creation, system upgrades, and audit traceability. When AI products are implemented in regulated and complex real-world businesses, relying on mature channels and secure access control to genuinely reduce labor costs is crucial for building a solid competitive advantage. Simply applying general-purpose auxiliary tools and superficial automation will make it difficult to guarantee stable profitability. Therefore, the valuation logic for industry business platforms and intelligent applications in vertical scenarios should differ from that of ordinary conceptual AI products. The intelligent agent business presents a new challenge: Will AI not only change production models but also reshape transaction flow paths? Visa has launched its Smart Commerce initiative, aiming to leverage its APIs, industry standards, and security mechanisms to enable smart agents to complete transactions on behalf of individual consumers and businesses. Reuters reports that Visa has partnered with Microsoft, Opna AI, IBM, Anthropic, Mistral, Bosch Intelligence, Samsung, and StripePay, among others, to develop AI-enabled business transactions. Mastercard launched its Smart Agent Payment project, defining it as a secure payment system for smart agent-based business scenarios, and subsequently partnered with more merchants and platform service providers to expand supporting tools and services. While these initiatives do not yet prove that smart agents will dominate business transactions, they demonstrate that payment networks view smart agent settlement as a strategically viable area with practical value, rather than a niche speculative concept. Once smart agents reduce information search costs and allocate transaction flows based on price, fulfillment reliability, and settlement efficiency, the traffic patterns formed by user habits will gradually weaken. Intermediary service providers that heavily rely on user inertia and information asymmetry for profit will face pressure; payment channels that combine credit and identity verification, low fraud risk, dispute resolution capabilities, and ultra-fast settlement advantages will become increasingly valuable. This also means that stablecoins, tokenized deposits, and tokenized money market funds are no longer limited to the crypto space and are beginning to deeply integrate with artificial intelligence businesses. Stripe Payments positions stablecoins as an all-weather cross-border payment channel; Visa continues to expand its stablecoin settlement business; JPMorgan Chase and the Bank of Montreal are working on building an institutional-grade tokenized cash system. A feasible development path is that while bank cards will remain the primary terminal scenario for ordinary users, tokenized cash, stablecoins, and bank deposit tokens will be widely used in the intermediate circulation links of machine interaction. These assets are particularly advantageous when intelligent agents optimize costs, accelerate transactions, achieve programmable operation, and enable all-weather asset-backed transfers. Currently, there is insufficient data to determine whether these changes will be fully implemented in the retail sector or only concentrated in corporate fund management, inter-company transactions, and capital market businesses. Stablecoins and Tokenized Cash: Stablecoins are a core indicator for testing whether the imitation of crypto concepts can be solidified into underlying infrastructure. This is because stablecoins directly enter payment settlement, fund management, cross-border remittances, trading margins, and global dollar intermediary business, directly competing with traditional finance. As of May 2026, the stablecoin market has already achieved significant influence, but industry concentration is high, and the profit structure is clearly defined. Industry estimates place the global stablecoin market at approximately $315 billion to $320 billion, with dollar-pegged stablecoins dominating. On May 18, 2026, Circle platform data showed that USDC circulation reached $76.8 billion; as of March 31, Tether's related liabilities were approximately $183 billion, and Reuters and the Bank for International Settlements reported that USDT circulation approached $190 billion in April. The two major issuing institutions together hold over 80% of the market share, exhibiting infrastructure-like characteristics rather than fragmented niche businesses. Circle's operational structure is standardized, with a clear institutional nature. USDC claims to be fully backed by highly liquid cash and equivalent assets, and can be exchanged for US dollars at a 1:1 ratio. The vast majority of its reserve assets are held in the Circle Reserve Fund, managed by BlackRock. This fund is a 2a-7 Rule government money fund registered with the U.S. Securities and Exchange Commission, and its assets are held in custody by Bank of New York Mellon. Filing documents show that the cash portion of the reserves is held in a separate account, with equity belonging to stablecoin holders. Data as of December 31, 2025, shows that 88% of USDC's reserve assets were held in this reserve fund. The Q1 2026 financial report disclosed that, driven by the expansion of circulation, reserve asset income reached $653 million, a year-on-year increase of 17%. The entire debt-based operating model exhibits typical institutional characteristics: audited financial statements, regulated reserve assets, a clearly defined custodian entity, and returns closely linked to short-term US dollar interest rates. However, USDT's operating structure is highly controversial. A Q1 2026 audit report issued by the international accounting firm BDO shows that its liabilities are approximately $183.5 billion, total assets are $191.8 billion, and reserve buffer funds are $8.23 billion. This includes approximately $141 billion in directly and indirectly held US short-term Treasury bonds, $20 billion in physical gold reserves, and approximately $7 billion in Bitcoin holdings. USDT has initiated a comprehensive audit process but has not yet issued a complete audit report; this distinction is crucial for institutional investors. While its market size and circulation coverage are undeniable, its information disclosure standards are far inferior to those of standardized listed entities like Circle. The fundamental differences between the two directly determine their respective valuation acceptance by regulatory agencies, banks, and conservative investors. Therefore, the reserve structure is far more crucial than the token's external form. Payment-type stablecoins are private liability assets, operating like fiat cash in programmable finance scenarios. Their stable and scalable development depends on redemption guarantees, asset liquidity under extreme market conditions, governance rules, legal enforcement, sanction control capabilities, and the composition of reserve assets. The Bank for International Settlements believes that stablecoins fail to meet the three core standards of currency: monetary uniformity, supply elasticity, and asset security, and has repeatedly warned that a significant expansion of stablecoin scale could trigger runs and low-price sell-offs of reserve assets. Related research also confirms that changes in the size of stablecoins affect the pricing of low-risk assets and the demand for short-term government bonds. The current focus is no longer on whether stablecoins belong to crypto assets, but rather on whether this new type of private monetary system can avoid the inherent vulnerabilities of past private monetary systems. The International Monetary Fund (IMF) maintains a relatively neutral stance, acknowledging that stablecoins can accelerate and reduce costs, and broaden the coverage of global financial services. However, it also warns that they can easily trigger currency substitution, capital volatility, market fragmentation, and ambiguous legal definitions, potentially exacerbating macroeconomic and financial risks in economies with weak monetary systems. The European Central Bank (ECB) emphasizes the issue of monetary sovereignty. In her May 2026 speech, Christine Lagarde advocated for distinguishing between financial functions and instruments; a research paper from the ECB points out that dollar-denominated stablecoins create new global channels for low-risk asset circulation, diverting bank deposits and forcing banks to rely more on interbank lending, thus weakening the effectiveness of monetary policy transmission. Following the enactment of the GENIUS Act in the United States, the Federal Reserve views payment-type stablecoins and tokenized deposits as potential payment innovations, while continuously monitoring the risks of bank runs, anti-money laundering controls, and the disintermediation of banking operations. In short, stablecoins have moved beyond the realm of speculative assets and become a key target of policy control in various countries. The relevant legal system in the United States has also been improved simultaneously. Documents from the Treasury Department and the Federal Reserve show that the GENIUS Act officially took effect on July 18, 2025, establishing a federal-level regulatory framework for stablecoin payments. The Treasury Department's detailed implementation rules for 2026 stipulate that compliant stablecoin issuers will be subject to the Bank Secrecy Act, similar to financial institutions, and will fulfill their sanction compliance obligations; issuers with a scale of less than $10 billion can meet certain conditions and be subject to similar regulatory rules in various states. In April 2025, the U.S. Securities and Exchange Commission stated that compliant stablecoins that meet the standards of full reserves and one-to-one redemption are not considered securities. The implementation of these policies has significantly reduced the uncertainty surrounding stablecoin regulation. Distribution channels have become the next key factor in determining success. Visa data shows that as of April 2026, the annualized scale of stablecoin settlement business reached $7 billion, expanding to five new blockchain networks; previously, its annualized settlement volume in the United States exceeded $3.5 billion, and Reuters statistics in January 2026 showed $4.5 billion. Mastercard launched its full-process stablecoin payment service in 2025 and acquired BVNK in March 2026, connecting on-chain payments with fiat currency channels, focusing on stablecoin, tokenized deposits, and tokenized asset business. Stripe Payments, after acquiring the Bridge platform, partnered with Visa to launch a stablecoin issuance service, offering stablecoin fund management and corporate treasury functions in 101 countries. Its 2025 annual report shows that the annual stablecoin payment transaction volume doubled to approximately $400 billion, and the transaction volume on the Bridge platform increased more than threefold. It's clear that industry winners aren't necessarily just token issuers. Network platforms with integrated scheduling capabilities and merchant circulation interfaces are better positioned to drive the large-scale application of assets. The competitive landscape is no longer limited to the battle between stablecoins and traditional banks. PayPal is expanding its own stablecoin, PYUSD, to physical consumption and points redemption scenarios, and plans to leverage the Stellar blockchain for cross-border payments and financing. Coinbase is building a commercial stablecoin payment system, stating in Q1 2026 that USDC circulation growth and Layer 2 network smart agent stablecoin trading have become core business growth drivers. JPMorgan Chase's bank token, JPM Coin, relies on bank deposits for programmable payment settlement, fundamentally different from stablecoins issued by non-bank institutions. BlackRock's 2026 Chairman's Letter disclosed that its tokenized government bond fund ranked first globally in size, and the company managed $65 billion in stablecoin reserves. Various types of monetary assets coexist in the market, with different issuers, business boundaries, profit models, and circulation channels. For those in the banking and payment industries, there is no absolutely superior category. Asset interaction interfaces, reserve structures, and regulatory boundaries are the core factors determining profit distribution. Stablecoins have long transcended their speculative crypto attributes; the underlying reality is a competition among various parties for the positioning of programmable cash within the financial system, vying for the returns on deposited funds and control over user traffic. The future landscape of capital flows will move towards public blockchains, bank token deposits, fund-encapsulated products, card organization systems, or a multi-faceted, integrated monetary model.

Analytical Framework and Strategic Implications

The most pragmatic evaluation criterion for applying the imitation premium theory is to pose a rigorous stress test question: After an asset price plummets by 70%, can its core value be preserved?

If only trading codes, community buzz, asset shells, and promotional concepts remain, it's superficial hype; if payment channels, reserve systems, compliance boundaries, switching costs, business processes, and physical hardware barriers still exist, then it possesses deep, substantial value.

This evaluation criterion is applicable to venture capital projects, growth-oriented companies, listed companies, infrastructure, and digital assets, and can eliminate the emotional interference brought about by market rallies, directly addressing the essential value of assets.

This checklist deliberately avoids category-based labeling. Even if a company is involved in artificial intelligence, crypto assets, stablecoins, or financial infrastructure, its foundation remains unstable if it fails to master irreplaceable core components. Conversely, seemingly niche or ordinary businesses can occupy a core position in the industry by controlling cost advantages, payment credibility, packaging and testing capabilities, or compliant distribution channels. This also illustrates that fundamental analysis of individual stocks and companies is far more crucial than judging the popularity of industry themes. The most common analytical pitfall in 2026 is judging a company's core value solely based on the relevance of its subject matter. The next phase of the imitation premium will not only affect existing transaction assets but will also be reflected in the new targets that the capital market will soon accept. By May 2026, the market is no longer simply speculating on the potential development of artificial intelligence. Nvidia's latest quarterly revenue reached $81.6 billion, with data center revenue at $75.2 billion. The company gave a revenue forecast of $91 billion for the next quarter, while also receiving approval for an $80 billion stock buyback program and significantly increasing its dividend. This impressive performance makes extreme arguments such as "artificial intelligence is a bubble" difficult to justify, and the market's focus has shifted accordingly. Now that genuine industry demand has emerged, investors need to identify which targets truly grasp the core value of the industry. This identification process is becoming increasingly urgent as private equity firms in the artificial intelligence and infrastructure sectors gradually seek IPOs. SpaceX has filed for its initial public offering (IPO), with Reuters estimating its valuation at approximately $1.75 trillion. Its shareholding structure ensures that Musk maintains firm control over voting decisions. This IPO target incorporates multiple market concepts, historically making it difficult for investors to uniformly price it: rocket and aerospace, Starlink satellite network, artificial intelligence infrastructure, xAI business layout, asset-heavy operating model, founder-centric centralized management, and long-term value option attributes. Anthropic becomes another case study. The company is reportedly about to achieve its first profitable quarter, with second-quarter revenue projected at $10.9 billion and operating profit at $559 million. Meanwhile, corporate investment in computing power remains high, including a $1.25 billion monthly computing power agreement with SpaceX until May 2029. This highlights a core contradiction in the cutting-edge AI industry: commercial revenue is steadily increasing, but computing power operating costs remain stubbornly high. Therefore, the imitation premium is no longer just a simple valuation concept, but rather relates to the overall market structure. The capital market is about to welcome a batch of giant listed companies whose core selling points are no longer limited to performance growth, but rather their entry into the next-generation industry's underlying tracks such as artificial intelligence, aerospace, computing power services, internet communications, and intelligent commerce. These companies possess real business value, products with industry influence, and broad market development potential. However, the key question is whether investors who purchase shares are acquiring core industry rights or merely buying highly liquid concept stocks. Historical trends show similar patterns. Investors who invested in railways recognized the trend of transportation transformation, those who invested in telecommunications anticipated rising bandwidth demand, and those who entered the internet sector saw the trend of online business restructuring. The market's judgment was not wrong in its direction; the mistakes often lay in the selection of targets: companies favored by capital did not truly enjoy the actual benefits brought by industry development. In some scenarios, infrastructure survived, but the companies' capital structures collapsed; in other areas, core profit-making links ultimately went to other companies, and early popular IPOs exhausted their conceptual premiums. The artificial intelligence industry is entering a similar development stage. In the early stages of the industry, funds concentrated on core computing power targets with the highest visibility; currently, value is gradually spreading to chip packaging, network communication, memory chips, energy supply, grid access, data center operation and maintenance, cloud service distribution, and enterprise business system management; in the future, a wave of IPOs of cutting-edge technology platform companies will arrive, and their valuations will depend on the level of the industry chain they actually control. The market needs to clearly distinguish between several business models: self-developed computing power, leased computing power, reselling computing power, computing power investment and financing, and simple conceptual packaging. The intrinsic value of these is fundamentally different. The stablecoin industry is also undergoing a simultaneous reshuffle. In the early stages, the industry competed on token market awareness; now, the core competition has shifted to the quality of liabilities and assets. Payment-type stablecoins, tokenized money market funds, bank token deposits, and central bank digital currencies may seem similar in their user experience, but they actually belong to completely different asset and liability categories. What can sustainably command a premium is not the name "stablecoin" itself, but rather the transparency of reserve assets, credibility of redemption and fulfillment, compliance qualifications, circulation coverage, settlement application scenarios, and the degree of integration with corporate fund management and asset-backed transactions. Therefore, the industry trend from 2026 to 2028 should not be simply judged as boom or bust. The essence is a shift from speculative hype based on scarce themes to verification of the actual structure of enterprises. In the first stage, there are few investable targets, and capital only pays for thematic concepts. In the second stage, funds begin to examine whether themes can be converted into stable profits. In the third stage, the secondary market completely strips away the conceptual veneer and identifies the true core value of operations. The industry development trend will not disappear, but the value of targets will diverge. Nvidia is expected to continue to maintain its leading position in the industry, while other AI-related targets will see a revaluation. Anthropic, while confirming real market demand, also highlights the high operating costs of cutting-edge AI technologies. Even if SpaceX joins the ranks of the world's top companies, investors will face multiple risks: corporate governance model, heavy asset investment, losses in AI business, fluctuations in the profitability of space projects, and long-term value uncertainty. The stablecoin sector is steadily empowering payment and collateralization scenarios, while issuers with weak qualifications will lose their premium advantage. This shows that the market is not lacking in rationality, but rather tends to prioritize pricing for highly recognizable targets before considering their long-term sustainability. Capital first buys into targets that can enter future sectors, and then assesses whether the company controls industry barriers, core assets and liabilities, distribution channels, cost advantages, and compliance permissions. This is also the core risk hidden in the new round of IPOs. The public market will soon absorb not only ordinary companies, but also emerging entities that encapsulate the future: artificial intelligence industry clusters, aerospace infrastructure, intelligent agent programs, tokenized currencies, private settlement systems, and large-scale intelligent business platforms. Some of these targets are truly deserving of their long-term premium value, while others can only serve as trading vehicles and cannot truly grasp the core of the industry. The future development trend is clearly foreseeable, a fact already confirmed by mid-2026. The real key lies in whether the equity sold in listed companies truly represents control over the future industrial structure, or merely a highly anticipated conceptual symbol.